Networks can be seen as an unstructured data source, requiring specific methods in order to extract network topology and be able to incorporate it into prediction models. Some approaches to learning on networks are based on matrix factorization including spectral clustering [1]} and learning with modularity matrix [2]}. More advanced methods are based on learning by performing random walks on a networks, e.g., DeepWalk [3]} and node2vec [4]}. Network topology can be incorporated into the model by extracting centrality information using PageRank-like algorithms which have been proved to be beneficial for, e.g., credit risk prediction in multilayer networks [5]}. The enhancements in deep learning have brought GNNs to the forefront of the field where they demonstrate cutting-edge performance [6]}. The general design pipeline of GNNs includes the steps of specifying the network type and scale, deciding on the task type and building the model using carefully designed computational modules [6]}.
